Because of continuous changes to the geometry and configuration of road and other transportation networks, mapping-related service providers (e.g., map data providers, navigation service providers, etc.) face significant technical challenges to creating and maintaining up-to-date map data. One area of development has been related to generating, updating, and/or analyzing map data through use of raw location data such as probe points collected by devices and/or vehicles equipped with sensors to report location, heading, speed, time, etc. as they travel. As part of this process, map-matchers (e.g., point-based map-matchers) are used to process the probe points to identify the correct road or path on which a probe device or vehicle is traveling, and to determine the device's location on that road or path. However, current map-matchers can often encounter issues of accuracy, scalability, and/or efficiency, particularly when processing high volumes of probe points and/or when processing probe points in real-time, particularly when these current map-matchers rely on empirical heuristics or generic assumptions that may or may not apply to the probe points being evaluated.